Pages that link to "Item:Q4641709"

The following pages link to Optimization Methods for Large-Scale Machine Learning (Q4641709):

Displaying 50 items.

• A gradient descent method for solving a system of nonlinear equations (Q2213706) (← links)
• Generalized gradients in dynamic optimization, optimal control, and machine learning problems (Q2215292) (← links)
• Optimization for deep learning: an overview (Q2218095) (← links)
• A review on deep learning in medical image reconstruction (Q2218098) (← links)
• How can machine learning and optimization help each other better? (Q2218099) (← links)
• Bi-fidelity stochastic gradient descent for structural optimization under uncertainty (Q2221705) (← links)
• Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning (Q2222332) (← links)
• Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders (Q2223001) (← links)
• A linearly convergent stochastic recursive gradient method for convex optimization (Q2228399) (← links)
• Backtracking gradient descent method and some applications in large scale optimisation. II: Algorithms and experiments (Q2234294) (← links)
• Neural network regression for Bermudan option pricing (Q2239248) (← links)
• Kernel-based online regression with canal loss (Q2242215) (← links)
• Accelerated gradient sliding for minimizing a sum of functions (Q2243755) (← links)
• A review on deep reinforcement learning for fluid mechanics (Q2245392) (← links)
• Convergence of online mirror descent (Q2278461) (← links)
• Non-asymptotic guarantees for sampling by stochastic gradient descent (Q2290072) (← links)
• Feature uncertainty bounds for explicit feature maps and large robust nonlinear SVM classifiers (Q2294606) (← links)
• Accelerated proximal incremental algorithm schemes for non-strongly convex functions (Q2297863) (← links)
• Hyper-parameter optimization for support vector machines using stochastic gradient descent and dual coordinate descent (Q2308188) (← links)
• Stochastic sampling for deterministic structural topology optimization with many load cases: density-based and ground structure approaches (Q2310028) (← links)
• MgNet: a unified framework of multigrid and convolutional neural network (Q2316958) (← links)
• Deep relaxation: partial differential equations for optimizing deep neural networks (Q2319762) (← links)
• Data science vs. statistics: two cultures? (Q2329839) (← links)
• A semismooth Newton method for support vector classification and regression (Q2419554) (← links)
• Generalized forward-backward splitting with penalization for monotone inclusion problems (Q2423787) (← links)
• Exploiting negative curvature in deterministic and stochastic optimization (Q2425164) (← links)
• Distributed nonconvex constrained optimization over time-varying digraphs (Q2425183) (← links)
• An accelerated variance reducing stochastic method with Douglas-Rachford splitting (Q2425236) (← links)
• Global optimization issues in deep network regression: an overview (Q2633536) (← links)
• Stochastic quasi-Newton with line-search regularisation (Q2664231) (← links)
• Optimal randomized classification trees (Q2668720) (← links)
• Efficient and sparse neural networks by pruning weights in a multiobjective learning approach (Q2669736) (← links)
• A framework for randomized time-splitting in linear-quadratic optimal control (Q2671271) (← links)
• The exact worst-case convergence rate of the gradient method with fixed step lengths for \(L\)-smooth functions (Q2673524) (← links)
• Stochastic proximal subgradient descent oscillates in the vicinity of its accumulation set (Q2679007) (← links)
• DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations (Q2681099) (← links)
• Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions (Q2693789) (← links)
• A limited-memory trust-region method for nonlinear optimization with many equality constraints (Q2695671) (← links)
• An abstract convergence framework with application to inertial inexact forward-backward methods (Q2696904) (← links)
• Inexact gradient projection method with relative error tolerance (Q2696906) (← links)
• Distributed stochastic gradient tracking methods with momentum acceleration for non-convex optimization (Q2696917) (← links)
• On the asymptotic rate of convergence of stochastic Newton algorithms and their weighted averaged versions (Q2696929) (← links)
• Halting time is predictable for large models: a universality property and average-case analysis (Q2697399) (← links)
• Parallel Optimization Techniques for Machine Learning (Q3300501) (← links)
• (Q4552074) (← links)
• (Q4558473) (← links)
• Nonasymptotic convergence of stochastic proximal point algorithms for constrained convex optimization (Q4558525) (← links)
• (Q4558562) (← links)
• ASTRO-DF: A Class of Adaptive Sampling Trust-Region Algorithms for Derivative-Free Stochastic Optimization (Q4561224) (← links)
• Adaptive Sampling Strategies for Stochastic Optimization (Q4562248) (← links)